Previous work on dependency parsing used various kinds of combination models but a systematic analysis and comparison of these approaches is lacking. In this paper we imple- mented such a study for English dependency parsing and find several non-obvious facts: (a) the diversity of base parsers is more important than complex models for learning (e.g., stack- ing, supervised meta-classification), (b) ap- proximate, linear-time re-parsing algorithms guarantee well-formed dependency trees with- out significant performance loss, and (c) the simplest scoring model for re-parsing (un- weighted voting) performs essentially as well as other more complex models. This study proves that fast and accurate ensemble parsers can be built with minimal effort.

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